Diffusion MRI (dMRI) is one of the main neuroimaging tools used to probe the microstructural organisation in the living human brain. Multiple metrics derived from dMRI are used today in research studies to detect and locate differences in the brain between different populations. Most of the metrics used today are based on diffusion tensor imaging (DTI), although newer metrics based on more complex models have been also proposed to further discriminate the rich information encoded in the diffusion signal. Nevertheless, in clinical studies, an important criterion to choose between any of the available metrics is sensitivity to detect pathological differences.

Results show that the inter-subject variability (and therefore the sensitivity) of most diffusion metrics seems to vary quite widely across different brain regions (Vollmar et al. 2010). Although the biological factors behind such regional variability remain undetermined, they may be related to the diversity of the microstructural organisation of neural tissue. In this work, we use computational simulations to compare the effect that different white matter configurations have on the sensitivity of existing metrics of diffusion and anisotropy.

Two main configurations were simulated. A first reference set of diffusion data was modelled as one single fibre population and one isotropic compartment with volume fraction of 0.9 and 0.1 respectively. A second reference set was modelled using two populations of fibres with a 0.45 fibre volume fraction each and an additional isotropic compartment with a remaining volume fraction of 0.1. The crossing angle between the two fibre populations was modelled at 0°, 15°, 30°, 45°, 60°, 75° and 90°. For each fibre population we assumed Axial Diffusivity of 1.3x10-3 mm2/s and Radial Diffusivity of 0.30x10-3 mm2/s. The diffusivity of the isotropic component was set equal to 0.7x10-3 mm2/s. To simulate then intra-group variability we generate a synthetic distribution for each fibre by randomly varying the fibre volume fractions, Axial and Radial Diffusivity around their reference values using a common coefficient of variation of 0.025 to reproduce a plausible biological variability.

To generate paired groups (i.e. control group vs test group) on which to evaluate the sensitivity of diffusion metrics, for each configuration in the test group we changed the radial diffusivity in one of the fibre populations. We defined three diffusion change levels equal to 0.33, 0.35 or 0.37x10-3 mm2/s. A Monte Carlo approach was then applied to obtain data equivalent to 10000 voxels per group by independently sampling from the synthetic distribution. For each configuration, the diffusion-weighted signal was generated along 6 non-diffusion weighted and 60 diffusion directions with a SNR of 20 and three b-values: 1000, 2000 and 3000 s2/mm. In total, 72 different configurations were simulated.

Sensitivity was evaluated on the following DTI metrics(Pierpaoli & Basser 1996): Fractional Anisotropy (FA), Mean Diffusivity (MD) and Radial Diffusivity (RD). In addition, General Anisotropy (GA)(Özarslan et al. 2005), General Fractional Anisotropy (GFA)(Tuch 2004) and Anisotropic Power (AP)(Dell'Acqua et al. 2014) were also evaluated. For each paired group of simulations, the means and standard deviations corresponding to each metric were used to compute the minimum sample size required to detect a difference on the group means based on an independent t-test with a statistical significance α=0.05 (two-sided) and statistical power 1-β = 0.85.

Overall, the number of subject required to detect the same biological change using the same source data changed from metric to metric. As expected, in the single fibre configuration the sensitivity of all anisotropy and diffusivity metrics showed comparable results with the exception of GFA (Fig 1, A and C). In contrast, the results where more varied in the case of two-fibres configuration where metrics were substantially affected by the amplitude of the crossing angle (Fig 1, B and D). Different number of subjects seems to be therefore required to detect the same biological change as consequence of the different underlying microstructural configuration. Finally, the b-value plays an important role on the final sensitivity of the metrics. As an example, for the particular case of AP, a higher b-value improves sensitivity while also reducing the effect of crossing angle.In this study we used simple simulations to verify that sensitivity of diffusion metrics to the same biological changes are substantially and differently affected by the underlying microstructural organization and acquisition parameters. Therefore, different brain regions may show different sensitivity levels to similar biological changes. As a consequence, traditional diffusion metrics may fail to consistently detect uniform changes along white matter tracts (e.g. axonal degeneration). Further studies are now required to better characterize sensitivity of different and more complex metrics to specific biological changes.